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Technical Approaches

Multimodal Transformers with Contrastive Learning
(MTC-AP)

Known for their ability to handle various media formats such as images, text, and audio, MTC-AP models utilize contrastive learning to distinguish legitimate content from pirated versions. This technique trains the system by learning from differences between authentic and unauthorized material, improving detection accuracy.

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Generative Adversarial Network for Anomaly Detection in Copyrighted Content
(GAN-AD-CC)

GANs are used to generate synthetic pirated content, expanding the model’s training diversity. This enhances its ability to detect unusual patterns in potential infringements. The “Anomaly Detection” component focuses on recognizing deviations from standard, legitimate content behaviors.

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Federated Anti-Piracy Learning with Blockchain-based Trust
(FAPL-BT)

FAPL-BT allows training data to remain on individual devices or platforms while the model benefits from collective learning. Blockchain ensures secure and transparent access control and copyright validation, enabling trustworthy anti-piracy operations without central data storage.

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Continuously Learning Anti-Piracy Framework with Active Learning
(CLAP)

CLAP leverages active learning, where the model selectively identifies the most valuable data samples for further training. This adaptive method improves the system’s robustness over time and ensures it stays effective against new piracy methods.

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Explainable Large Model for Copyright Infringement Detection
(XL-MID)

XL-MID not only detects infringement but also explains the reasoning behind its classification. By integrating explainability with large-scale language models, it offers comprehensive, transparent analysis of suspect content across multiple modalities.

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Real-Time Piracy Pattern Mining with Adaptive Feedback
(RPM-AF)

RPM-AF continuously mines real-time piracy patterns across platforms and integrates adaptive feedback from detection systems and human reviewers. This closed-loop mechanism boosts model responsiveness and accuracy, enabling proactive defense against emerging infringement tactics.

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Our team of legal experts

60% FASTER TAKEDOWNS

TIME TO LIVE REDUCED BY 17 DAYS

Every minute infringing activity is in market your brand is open to risk.
AxaruAI reduces the total lifecycle of brand abuse from the time of
detection to enforcement, all the way through compliance.

+182%

REVIEW VOLUME

+182% more reviews YoY

18x

TIME TO REVIEW

18x faster review time YoY

+30%

TAKEDOWN VOLUME

+30% more takedowns YoY

Our Results

Detections

154M

DETECTIONS

We find what most can't. 186% faster than most platforms.

Executive Rate

96%

EXECUTION RATE

API integrates with 11 of the world’s top platforms.

IP Protection

47K+

IP PROTECTED

Trusted by top Fortune 500 brands worldwide.